Recent top-k computation efforts explore the possibility of revising various sorting algorithms to answer top-k queries on GPUs. These endeavors, unfortunately, perform significantly more work than needed. This paper introduces Dr. Top-k, a Delegate-centric top-k system on GPUs that can reduce the top-k workloads significantly. Particularly, it contains three major contributions: First, we introduce a comprehensive design of the delegate-centric concept, including maximum delegate, delegate-based filtering, and β delegate mechanisms to help reduce the workload for top-k up to more than 99%. Second, due to the difficulty and importance of deriving a proper subrange size, we perform a rigorous theoretical analysis, coupled with thorough experimental validations to identify the desirable subrange size. Third, we introduce four key system optimizations to enable fast multi-GPU top-k computation. Taken together, this work constantly outperforms the state-of-the-art.
@article{arxiv.2109.08219,
title = {Dr. Top-k: Delegate-Centric Top-k on GPUs},
author = {Anil Gaihre and Da Zheng and Scott Weitze and Lingda Li and Shuaiwen Leon Song and Caiwen Ding and Xiaoye S Li and Hang Liu},
journal= {arXiv preprint arXiv:2109.08219},
year = {2021}
}
Comments
To be published in The International Conference for High Performance Computing, Networking, Storage and Analysis (SC 21)